Simulation of log fitness change
Laure Olazcuaga, Nicolas Rode
04-December-2020
1 DATA
## Longitudinal data
data_sum <- loadlongitudinaldata(dataset = "DATA_Adults_G1G29.csv", rm_generation1 = 1,rm_generation2 = 7,rm_generation3 = 29)
## Phenotyping steps
data_G0 <- loadfitnessdata(dataset = "Selection_Phenotypage_G0_G7_G8.csv", generation = "G1")
data_G7 <- loadfitnessdata(dataset = "Selection_Phenotypage_G0_G7_G8.csv", generation = "G7")
data_G29 <- loadfitnessdata(dataset =
"PERFORMANCE_Comptage_adultes_G13G14G15G16G17G18G19G20G21G22G23G24G25G26G27G28G29.csv",
generation = "29")
#Compute log change
data_logchange<-computelogchange(fitness_dataset_intermediate = data_G7, fitness_dataset_final = data_G29)
#Lines: CE2 and CR2 do not pass Geary's test (the threshold of a standardized mean greater than 3)
data_logchange$Line_type <- ifelse(data_logchange$Line == "CR2"| data_logchange$Line == "CE2", "solid","dashed")
#Formatting for plotting pairwise of fitness difference
EC_Cran_Cher_G7<-formattingplotpair(data_logchange, "7", "Cherry", "Cranberry")
EC_Straw_Cran_G7<-formattingplotpair(data_logchange, "7", "Cranberry", "Strawberry")
EC_Straw_Cher_G7<-formattingplotpair(data_logchange, "7", "Strawberry", "Cherry")
EC_Cran_Cher_G29<-formattingplotpair(data_logchange, "29", "Cherry", "Cranberry")
EC_Straw_Cran_G29<-formattingplotpair(data_logchange, "29", "Cranberry", "Strawberry")
EC_Straw_Cher_G29<-formattingplotpair(data_logchange, "29", "Strawberry", "Cherry")
#Formatting for plot pool
TEMP_dataG29_CheCran <- formattinglogchange(data_logchange, "29", "Cherry", "Cranberry")
TEMP_dataG29_CranStraw <- formattinglogchange(data_logchange, "29", "Cranberry", "Strawberry")
TEMP_dataG29_StrawChe <- formattinglogchange(data_logchange, "29", "Strawberry", "Cherry")2 SUP: Pairwise of Fitness difference
2.1 Plot
#Limit axis
lim_G7<-max(abs(min(data_logchange[data_logchange$Generation=="7",]$logchange-
1.96*data_logchange[data_logchange$Generation=="7",]$sd_logchange, na.rm = T)),
max(data_logchange[data_logchange$Generation=="7",]$logchange+
1.96*data_logchange[data_logchange$Generation=="7",]$sd_logchange, na.rm = T))
lim_G29<-max(abs(min(data_logchange[data_logchange$Generation=="29",]$logchange-
1.96*data_logchange[data_logchange$Generation=="29",]$sd_logchange, na.rm = T)),
max(data_logchange[data_logchange$Generation=="29",]$logchange+
1.96*data_logchange[data_logchange$Generation=="29",]$sd_logchange, na.rm = T))
#Plot
PAIR_StrawCran_G7<-ggplot(data = EC_Straw_Cran_G7,
aes(x = logchange_Strawberry,y = logchange_Cranberry, color=Fruit_s)) +
geom_errorbar(aes(ymin = logchange_Cranberry-1.96*sd_logchange_Cranberry,
ymax = logchange_Cranberry+1.96*sd_logchange_Cranberry, linetype=Line_type),
width=0.02,size=0.2,alpha=1) +
geom_errorbarh(aes(xmin = logchange_Strawberry-1.96*sd_logchange_Strawberry,
xmax = logchange_Strawberry+1.96*sd_logchange_Strawberry, linetype=Line_type),
height=0.02,size=0.2,alpha=1) +
geom_point(shape=21, size=3, fill = "#ffffff", stroke=1.3) +
xlab("Fitness difference\nin strawberry") +
ylab("Fitness difference\nin cranberry") +
xlim(-lim_G7,lim_G7) +
ylim(-lim_G7,lim_G7) +
scale_color_manual(values=c("#BC3C6D", "#FDB424", "#3FAA96")) +
theme(axis.title.x = element_text(size=10, colour = "#3FAA96"),
axis.title.y = element_text(size=10, colour = "#FDB424")) +
theme_LO_sober
PAIR_StrawCran_G7
PAIR_StrawChe_G7<-ggplot(data = EC_Straw_Cher_G7,
aes(x = logchange_Cherry,y = logchange_Strawberry, color=Fruit_s)) +
geom_errorbar(aes(ymin = logchange_Strawberry-1.96*sd_logchange_Strawberry,
ymax = logchange_Strawberry+1.96*sd_logchange_Strawberry, linetype=Line_type),
width=0.02, size=0.2, alpha=1) +
geom_errorbarh(aes(xmin = logchange_Cherry-1.96*sd_logchange_Cherry,
xmax = logchange_Cherry+1.96*sd_logchange_Cherry, linetype=Line_type),
height=0.02, size=0.2, alpha=1) +
geom_point(shape=21, size=3, fill = "#ffffff", stroke=1.3) +
ylab("Fitness difference\nin strawberry") +
xlab("Fitness difference\nin cherry") +
scale_color_manual(values=c("#BC3C6D", "#FDB424", "#3FAA96")) +
xlim(-lim_G7,lim_G7) +
ylim(-lim_G7,lim_G7) +
theme(axis.title.x = element_text(size=10, colour = "#BC3C6D"),
axis.title.y = element_text(size=10, colour = "#3FAA96")) +
theme_LO_sober
PAIR_StrawChe_G7
PAIR_CheCran_G7<-ggplot(data = EC_Cran_Cher_G7,aes(x = logchange_Cranberry,y = logchange_Cherry,
color=Fruit_s)) +
geom_errorbar(aes(ymin = logchange_Cherry-1.96*sd_logchange_Cherry,
ymax = logchange_Cherry+1.96*sd_logchange_Cherry, linetype=Line_type),
width=0.02, size=0.2, alpha=1) +
geom_errorbarh(aes(xmin = logchange_Cranberry-1.96*sd_logchange_Cranberry,
xmax = logchange_Cranberry+1.96*sd_logchange_Cranberry, linetype=Line_type),
height=0.02, size=0.2, alpha=1) +
geom_point(shape=21, size=3, fill = "#ffffff", stroke=1.3) +
ylab("Fitness difference\nin cherry") +
xlab("Fitness difference\nin cranberry") +
xlim(-lim_G7, lim_G7) +
ylim(-lim_G7, lim_G7) +
scale_color_manual(values = c("#BC3C6D", "#FDB424", "#3FAA96")) +
theme_LO_sober +
theme(axis.title.x = element_text(colour = "#FDB424"),
axis.title.y = element_text(colour = "#BC3C6D"))
PAIR_CheCran_G7
################## Final phenotyping step
#Plot
PAIR_CranStraw_G29<-ggplot(data = EC_Straw_Cran_G29,
aes(x = logchange_Strawberry, y = logchange_Cranberry, color=Fruit_s)) +
geom_errorbar(aes(ymin = logchange_Cranberry-1.96*sd_logchange_Cranberry,
ymax = logchange_Cranberry+1.96*sd_logchange_Cranberry, linetype=Line_type),
width=0.02,size=0.2,alpha=1) +
geom_errorbarh(aes(xmin = logchange_Strawberry-1.96*sd_logchange_Strawberry,
xmax = logchange_Strawberry+1.96*sd_logchange_Strawberry, linetype=Line_type),
height=0.02,size=0.2,alpha=1) +
geom_point(aes(fill = Fruit_s),shape=21, size=3) +
xlab("Fitness difference\nin strawberry") +
ylab("Fitness difference\nin cranberry") +
xlim(-lim_G29,lim_G29) +
ylim(-lim_G29,lim_G29) +
scale_color_manual(values=c("#BC3C6D", "#FDB424", "#3FAA96")) +
scale_fill_manual(values=c("#BC3C6D", "#FDB424", "#3FAA96")) +
theme(axis.title.x = element_text(size=10, colour = "#3FAA96"),
axis.title.y = element_text(size=10, colour = "#FDB424")) +
theme_LO_sober
PAIR_CranStraw_G29
PAIR_StrawChe_G29<-ggplot(data = EC_Straw_Cher_G29,
aes(x = logchange_Cherry,y = logchange_Strawberry, color=Fruit_s)) +
geom_errorbar(aes(ymin = logchange_Strawberry-1.96*sd_logchange_Strawberry,
ymax = logchange_Strawberry+1.96*sd_logchange_Strawberry, linetype=Line_type),
width=0.02,size=0.2,alpha=1) +
geom_errorbarh(aes(xmin = logchange_Cherry-1.96*sd_logchange_Cherry,
xmax = logchange_Cherry+1.96*sd_logchange_Cherry, linetype=Line_type),
height=0.02,size=0.2,alpha=1) +
geom_point(aes(fill = Fruit_s),shape=21, size=3) +
ylab("Fitness difference\nin strawberry") +
xlab("Fitness difference\nin cherry") +
scale_color_manual(values=c("#BC3C6D", "#FDB424", "#3FAA96")) +
scale_fill_manual(values=c("#BC3C6D", "#FDB424", "#3FAA96")) +
xlim(-lim_G29,lim_G29) +
ylim(-lim_G29,lim_G29) +
theme(axis.title.x = element_text(colour = "#BC3C6D"),
axis.title.y = element_text(colour = "#3FAA96")) +
theme_LO_sober
PAIR_StrawChe_G29
PAIR_CheCran_G29<-ggplot(data = EC_Cran_Cher_G29,
aes(x = logchange_Cranberry,y = logchange_Cherry, color=Fruit_s)) +
geom_errorbar(aes(ymin = logchange_Cherry-1.96*sd_logchange_Cherry,
ymax = logchange_Cherry+1.96*sd_logchange_Cherry, linetype=Line_type),
width=0.02,size=0.2,alpha=1) +
geom_errorbarh(aes(xmin = logchange_Cranberry-1.96*sd_logchange_Cranberry,
xmax = logchange_Cranberry+1.96*sd_logchange_Cranberry, linetype=Line_type),
height=0.02,size=0.2,alpha=1) +
geom_point(aes(fill = Fruit_s), shape=21, size=3) +
ylab("Fitness difference\nin cherry") +
xlab("Fitness difference\nin cranberry") +
xlim(-lim_G29,lim_G29) +
ylim(-lim_G29,lim_G29) +
scale_fill_manual(values=c("#BC3C6D", "#FDB424", "#3FAA96")) +
scale_color_manual(values=c("#BC3C6D", "#FDB424", "#3FAA96")) +
theme_LO_sober +
theme(axis.title.x = element_text(colour = "#FDB424"),
axis.title.y = element_text(colour = "#BC3C6D"))
PAIR_CheCran_G29
#Legend
TEMP_data_logchange_sum_Straw_Cher_G7<-data_logchange
TEMP_data_logchange_sum_Straw_Cher_G7$Pheno<-sample(c("Intermediate", "Final"),
length(data_logchange$logchange),
replace=TRUE)
PAIR_plot_legend<- ggplot(data=TEMP_data_logchange_sum_Straw_Cher_G7,
aes(x=Treatment, y=logchange, group=Fruit_s, colour=Fruit_s,shape=Pheno)) +
geom_errorbar(aes(ymin=logchange-1.96*sd_logchange, ymax=logchange+1.96*sd_logchange),
width=0.01,size=1) +
geom_point(size=3, stroke=1.3) +
scale_shape_manual(name = "Phenotyping step",
labels = c("Intermediate", "Final"),values=c(21,16)) +
scale_color_manual(name= "Selection fruit",
values=c("#BC3C6D", "#FDB424", "#3FAA96"),
label=c("Cherry", "Cranberry", "Strawberry")) +
theme_LO_sober
PAIR_legend<-lemon::g_legend(PAIR_plot_legend)
rm(TEMP_data_logchange_sum_Straw_Cher_G7)
PAIR_Fig_Sup <- cowplot::ggdraw() +
cowplot::draw_plot(PAIR_CheCran_G7+theme(legend.position = "none"),
x =0.00, y = 0.5, width = 0.22, height =0.45) +
cowplot::draw_plot(PAIR_StrawCran_G7+theme(legend.position = "none"),
x = 0.26, y = 0.5, width = 0.22, height = 0.45) +
cowplot::draw_plot(PAIR_StrawChe_G7+theme(legend.position = "none"),
x = 0.52, y = 0.5, width = 0.22, height = 0.45) +
cowplot::draw_plot(PAIR_legend, x = 0.80, y = 0.5, width = 0.1, height = 0.1) +
cowplot::draw_plot(PAIR_CheCran_G29+theme(legend.position = "none"),
x =0.00, y = 0, width = 0.22, height = 0.45) +
cowplot::draw_plot(PAIR_CranStraw_G29+theme(legend.position = "none"),
x = 0.26, y = 0, width = 0.22, height = 0.45) +
cowplot::draw_plot(PAIR_StrawChe_G29+theme(legend.position = "none"),
x = 0.52, y = 0, width = 0.22, height = 0.45) +
cowplot::draw_plot_label(c("Intermediate phenotyping step", "A", "B", "C", " ",
"Final phenotyping step", "D", "E", "F", " "),
x = c(0.26,0,0.26,0.52,0.82,0.2,0,0.26,0.52,0.82),
y = c(1.01,0.97,0.97,0.97,0.97,0.5,0.47, 0.47, 0.47, 0.47),
hjust = c(-0.25,-0.25,-0.25,-0.25,-0.25,-0.75,-0.75,-0.75,-0.75,-0.75),
vjust = c(1.5,1.5,1.5,1.5,1.5,1.5,1.5,1.5,1.5,1.5),
size = 14)
PAIR_Fig_Sup
cowplot::save_plot(file=here::here("figures", "SUPMAT_LogChange_Pairwise.pdf"), PAIR_Fig_Sup,
base_height = 15/cm(1), base_width = 30/cm(1), dpi = 610)3 SUP: Long vs Fitness
3.1 Data
#To consider fruit maternal effect and GF maternal effect: new plot
## Y-axis = log change = log(Fitness pheno)-log(fitness initial)
## X-axis = fitness temp / fitness initial
## example: for CEA during first phase
## log(mean (CE1 from G2 to G5) / mean (Cherry G2))
##To check with Nico: for x-axis: denominator=mean Cherry G2 or mean CE1 G2 (???????????)
####### Phenotyping steps
#Compute log change
data_logchange <- computelogchange(fitness_dataset_intermediate = data_G7, fitness_dataset_final = data_G29)
#Remove allopatric data
data_fitness_pheno <- data_logchange[data_logchange$SA==1,]
data_fitness_pheno <- data_fitness_pheno[, c("Line", "Fruit_s", "logchange", "sd_logchange")]
####### Longitudinal data
#Compute log change
data_logchange_long <- computelogchange_forlongdata(longitudinal_dataset = data_sum)
####### Merge data
data_compare_longitudinal_pheno <- merge(data_logchange_long,data_fitness_pheno,by=c("Line", "Fruit_s"))
data_compare_longitudinal_pheno <- droplevels(data_compare_longitudinal_pheno)
## Does not seem to be a correlation between measures
cor.test(data_compare_longitudinal_pheno$logchange[data_compare_longitudinal_pheno$Phase=="first_prepool"], data_compare_longitudinal_pheno$logchange_long[data_compare_longitudinal_pheno$Phase=="first_prepool"])##
## Pearson's product-moment correlation
##
## data: data_compare_longitudinal_pheno$logchange[data_compare_longitudinal_pheno$Phase == "first_prepool"] and data_compare_longitudinal_pheno$logchange_long[data_compare_longitudinal_pheno$Phase == "first_prepool"]
## t = 0.56297, df = 12, p-value = 0.5838
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4046042 0.6367930
## sample estimates:
## cor
## 0.1604117cor.test(data_compare_longitudinal_pheno$logchange[data_compare_longitudinal_pheno$Phase=="second_postpool"], data_compare_longitudinal_pheno$logchange_long[data_compare_longitudinal_pheno$Phase=="second_postpool"])##
## Pearson's product-moment correlation
##
## data: data_compare_longitudinal_pheno$logchange[data_compare_longitudinal_pheno$Phase == "second_postpool"] and data_compare_longitudinal_pheno$logchange_long[data_compare_longitudinal_pheno$Phase == "second_postpool"]
## t = 0.86532, df = 9, p-value = 0.4093
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3870859 0.7519973
## sample estimates:
## cor
## 0.2771425####### Geary test
#Lines: CE2 and CR2 do not pass Geary's test (the threshold of a standardized mean greater than 3)
data_compare_longitudinal_pheno$Line_type <- ifelse(data_compare_longitudinal_pheno$Line == "CR2"|
data_compare_longitudinal_pheno$Line == "CE2", "solid","dashed")3.2 Plot long vs fitness
PLOT_IntermediatePheno_LOGCHANGE<-ggplot(data=data_compare_longitudinal_pheno[data_compare_longitudinal_pheno$Phase=="first_prepool",],
aes(y=logchange,x=logchange_long, color=Fruit_s,shape=Fruit_s, linetype=Line_type)) +
geom_errorbar(aes(x = logchange_long,
ymin = logchange-(1.96*sd_logchange),
ymax = logchange+(1.96*sd_logchange)),
width=0.02,size=0.2,alpha=1) +
geom_errorbarh(aes(y = logchange,
xmin = logchange_long-1.96*sd_logchange_long,
xmax = logchange_long+1.96*sd_logchange_long),
height=0.02,size=0.2,alpha=1) +
geom_point(size=3, fill = "#ffffff", stroke=1.3) +
scale_color_manual(values=c("#BC3C6D", "#FDB424", "#3FAA96")) +
scale_shape_manual(values=c(21, 22, 24)) +
xlab("Fitness difference between average fitness across generations 13 to 26\nand average fitness across generations 2 to 5") +
ylab("Fitness difference between\nintermediate and initial phenotyping steps") +
labs(color="Selection fruit", shape="Selection fruit") +
geom_abline(intercept = 0, slope = 1, color="black", linetype="dashed", size=0.8) +
ylim(-1.9,0.9) +
xlim(-1.9,0.9) +
guides(linetype=FALSE) +
ggtitle("Intermediate phenotyping step vs. Phase 1") +
theme_LO_sober
PLOT_IntermediatePheno_LOGCHANGE 
PLOT_FinalPheno_LOGCHANGE <-
ggplot(data=data_compare_longitudinal_pheno
[data_compare_longitudinal_pheno$Phase=="second_postpool",],
aes(y=logchange, x=logchange_long,
color=Fruit_s, shape=Fruit_s, fill=Fruit_s, linetype=Line_type)) +
geom_errorbar(aes(x = logchange_long,
ymin = logchange-(1.96*sd_logchange),
ymax = logchange+(1.96*sd_logchange)),
width=0.02,size=0.2,alpha=1) +
geom_errorbarh(aes(y = logchange,
xmin = logchange_long-1.96*sd_logchange_long,
xmax = logchange_long+1.96*sd_logchange_long),
height=0.02,size=0.2,alpha=1) +
geom_abline(intercept = 0, slope = 1, color="black",
linetype="dashed", size=0.8) +
geom_point(size=3, stroke=1) +
scale_fill_manual(values=c("#BC3C6D", "#FDB424", "#3FAA96")) +
scale_color_manual(values=c("#BC3C6D", "#FDB424", "#3FAA96")) +
scale_shape_manual(values=c(21, 22, 24)) +
xlab("Fitness difference between average fitness across generations 13 to 26\nand average fitness across generations 2 to 5") +
ylab("Fitness difference between\nfinal and initial phenotyping steps") +
labs(color="Selection fruit", shape="Selection fruit", fill="Selection fruit") +
ylim(-0.5,1.7) +
xlim(-0.5,1.7) +
guides(linetype=FALSE) +
ggtitle("Final phenotyping step vs. Phase 3") +
theme_LO_sober
PLOT_FinalPheno_LOGCHANGE 
PLOT_Comparison_Pheno_Longitudinal_LOGCHANGE <- cowplot::ggdraw() +
cowplot::draw_plot(PLOT_IntermediatePheno_LOGCHANGE +
theme(axis.title.y = element_text(size=10),
axis.title.x = element_text(size=10)),
x = 0, y = 0.5, width = 1, height = 0.5) +
cowplot::draw_plot(PLOT_FinalPheno_LOGCHANGE +
theme(axis.title.y = element_text(size=10),
axis.title.x = element_text(size=10)),
x =0, y = 0, width = 1, height = 0.5) +
cowplot::draw_plot_label(c("A", "B"),
x = c(0,0), y = c(1,0.5),
size = 14)
PLOT_Comparison_Pheno_Longitudinal_LOGCHANGE
cowplot::save_plot(file=here::here("figures", "SUPMAT_LogChange_Pheno_Longitudinal.pdf"),
PLOT_Comparison_Pheno_Longitudinal_LOGCHANGE, base_height = 20/cm(1), base_width = 15/cm(1), dpi = 1200)3.3 Extra plot Maternal effects
m <- lm(logchange ~ logchange_long + offset(logchange_long),
data = data_compare_longitudinal_pheno[data_compare_longitudinal_pheno$Phase=="second_postpool",])
summary(m)##
## Call:
## lm(formula = logchange ~ logchange_long + offset(logchange_long),
## data = data_compare_longitudinal_pheno[data_compare_longitudinal_pheno$Phase ==
## "second_postpool", ])
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.33858 -0.19094 0.00374 0.12442 0.46801
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.1419 0.4433 -0.320 0.756
## logchange_long -0.6261 0.4321 -1.449 0.181
##
## Residual standard error: 0.2755 on 9 degrees of freedom
## Multiple R-squared: 0.07681, Adjusted R-squared: -0.02577
## F-statistic: 0.7488 on 1 and 9 DF, p-value: 0.4093#pval of logchange_long = 0.003 => slope is different of 1
#pval of Intercept = 0.80 => no evolution of maternal effect?
## Ratio maternal effects derived pop / maternal effect ancestral population
exp(coef(m)[1]) ## (Intercept)
## 0.8677455# proportion of adaptation due to maternal effects
data_compare_longitudinal_pheno$mateff <- (data_compare_longitudinal_pheno$logchange_long -
data_compare_longitudinal_pheno$logchange) / data_compare_longitudinal_pheno$logchange_long
data_compare_longitudinal_pheno## Line Fruit_s Phase logchange_long sd_logchange_long logchange
## 1 CE1 Cherry first_prepool 0.052171872 0.04369480 -0.27403677
## 2 CE2 Cherry first_prepool -0.141633419 0.05989994 -0.56171884
## 3 CE3 Cherry first_prepool 0.181479090 0.05900577 0.18528246
## 4 CE4 Cherry first_prepool 0.112182431 0.03403012 0.12791340
## 5 CEA Cherry second_postpool 1.286461806 0.03009355 0.14767000
## 6 CEB Cherry second_postpool 1.271123602 0.02927841 0.25038029
## 7 CEC Cherry second_postpool 1.328414009 0.02919479 0.35854062
## 8 CR1 Cranberry first_prepool -0.106472445 0.06244081 0.26983737
## 9 CR2 Cranberry first_prepool -0.131812582 0.02962644 -0.10684877
## 10 CR3 Cranberry first_prepool 0.223655846 0.04140920 0.05809882
## 11 CR4 Cranberry first_prepool -0.005509029 0.05739275 0.35156311
## 12 CR5 Cranberry first_prepool -0.128907603 0.07401524 0.18840336
## 13 CRA Cranberry second_postpool 0.803160627 0.02812570 -0.14885350
## 14 CRB Cranberry second_postpool 0.916989807 0.02973486 0.29719689
## 15 CRC Cranberry second_postpool 0.961100074 0.02673814 0.02703717
## 16 CRD Cranberry second_postpool 0.867039542 0.02951112 -0.15627744
## 17 CRE Cranberry second_postpool 1.067157543 0.02649123 0.72513486
## 18 FR1 Strawberry first_prepool 0.292467664 0.03244930 0.33051044
## 19 FR2 Strawberry first_prepool -0.344088555 0.06045692 0.09022737
## 20 FR3 Strawberry first_prepool -0.014070055 0.02785930 0.13335674
## 21 FR4 Strawberry first_prepool 0.033908974 0.02627773 0.58063316
## 22 FR5 Strawberry first_prepool -0.516039148 0.02443227 0.18650420
## 23 FRA Strawberry second_postpool 0.766018339 0.02732276 0.15709032
## 24 FRB Strawberry second_postpool 0.867962346 0.02567402 0.56023100
## 25 FRC Strawberry second_postpool 0.951223373 0.02672825 0.36640738
## sd_logchange Line_type mateff
## 1 0.25731181 dashed 6.25257685
## 2 0.57349396 solid -2.96600492
## 3 0.11827433 dashed -0.02095761
## 4 0.24311831 dashed -0.14022667
## 5 0.15742840 dashed 0.88521229
## 6 0.10098643 dashed 0.80302444
## 7 0.11692997 dashed 0.73009874
## 8 0.21318308 dashed 3.53433998
## 9 0.46435229 solid 0.18938869
## 10 0.13676999 dashed 0.74023116
## 11 0.12714934 dashed 64.81580042
## 12 0.14497197 dashed 2.46153801
## 13 0.11935520 dashed 1.18533465
## 14 0.15311973 dashed 0.67589946
## 15 0.11563812 dashed 0.97186852
## 16 0.11566948 dashed 1.18024257
## 17 0.12754300 dashed 0.32049878
## 18 0.08222221 dashed -0.13007514
## 19 0.17892718 dashed 1.26222138
## 20 0.16884574 dashed 10.47805368
## 21 0.13295026 dashed -16.12328880
## 22 0.25058617 dashed 1.36141484
## 23 0.14115599 dashed 0.79492616
## 24 0.12842826 dashed 0.35454458
## 25 0.12011698 dashed 0.61480406#Plot
PLOT_Maternaleffects<-ggplot(data=data_compare_longitudinal_pheno,
aes(y=mateff,x=logchange_long, color=Fruit_s, shape=Fruit_s, fill=interaction(Phase,Fruit_s))) +
geom_point(size=3, stroke=1.3) +
scale_fill_manual(name = "Phase",
values = c("#ffffff","#BC3C6D", "#ffffff","#FDB424", "#ffffff", "#3FAA96"),
breaks = c("first_prepool.Cherry", "second_postpool.Cherry"),
labels = c("Phase 1","Phase 3")) +
scale_color_manual(values=c("#BC3C6D", "#FDB424", "#3FAA96")) +
scale_shape_manual(values=c(21, 22, 24)) +
xlab("Fitness difference between average fitness\nacross generations 2 to 5 (13 to 26) and generation 2") +
ylab("Proportion of adaptation\ndue to evolution of maternal effects") +
labs(color="Selection fruit", shape="Selection fruit") +
guides(fill = guide_legend(override.aes = list(fill = c("white","black"), shape=21))) +
theme_LO_sober
PLOT_Maternaleffects 
cowplot::save_plot(file=here::here("figures", "SUPMAT_Maternal_effects.pdf"),
PLOT_Maternaleffects, base_height = 20/cm(1), base_width = 15/cm(1), dpi = 1200)4 SUP: Adaptation 8fruits
4.1 Longitudinal data
data_sum_allfruits <- loadlongitudinaldata_allfruits(dataset = "DATA_Adults_G1G29.csv", rm_generation_max = 5)
data_sum_allfruits_AN<-data_sum_allfruits[data_sum_allfruits$Generation!=1,]4.2 Analysis
#Models
mod_all_interaction <- lme4::lmer(fitness ~ Fruit_s*Generation + (1|Generation) + (1|Generation:Fruit_s),
weights = N, data = data_sum_allfruits_AN, REML = FALSE)
mod_all_generation<- lme4::lmer(fitness ~ Generation + (1|Generation) + (1|Generation:Fruit_s) ,
weights = N, data = data_sum_allfruits_AN, REML = FALSE)
mod_all_fruit_generation<- lme4::lmer(fitness ~ Fruit_s+Generation + (1|Generation) + (1|Generation:Fruit_s) ,
weights = N, data = data_sum_allfruits_AN, REML = FALSE)
mod_all_fruit<- lme4::lmer(fitness ~ Fruit_s + (1|Generation) + (1|Generation:Fruit_s) ,
weights = N, data = data_sum_allfruits_AN, REML = FALSE)
mod_all_line<- lme4::lmer(fitness ~ Line + (1|Generation) + (1|Generation:Fruit_s),
weights = N, data = data_sum_allfruits_AN, REML = FALSE)
mod_all_null<- lme4::lmer(fitness ~ 1 + (1|Generation) + (1|Generation:Fruit_s),
weights = N, data = data_sum_allfruits_AN, REML = FALSE)
mod_all_interaction_line_generation <- lme4::lmer(fitness ~ Line*Generation + (1|Generation) + (1|Generation:Fruit_s),
weights = N, data = data_sum_allfruits_AN, REML = FALSE)
mod_line_generation <- lme4::lmer(fitness ~ Line+Generation + (1|Generation) + (1|Generation:Fruit_s),
weights = N, data = data_sum_allfruits_AN, REML = FALSE)
MuMIn::model.sel(mod_all_interaction,mod_all_fruit_generation,mod_all_fruit,mod_all_null,mod_all_generation,mod_all_line,mod_all_interaction_line_generation,mod_line_generation)## Model selection table
## (Int) Frt_s Gnr Frt_s:Gnr Lin
## mod_all_interaction -1.2580 + -0.56070 +
## mod_all_fruit -2.4250 +
## mod_all_fruit_generation -2.3690 + -0.02678
## mod_all_line -2.3980 +
## mod_line_generation -2.1860 -0.08906 +
## mod_all_generation -1.8340 0.27440
## mod_all_null -0.9798
## mod_all_interaction_line_generation 1.0780 -1.60000 +
## Gnr:Lin family df logLik
## mod_all_interaction gaussian(identity) 19 -80.610
## mod_all_fruit gaussian(identity) 11 -92.408
## mod_all_fruit_generation gaussian(identity) 12 -92.306
## mod_all_line gaussian(identity) 43 -37.016
## mod_line_generation gaussian(identity) 44 -35.790
## mod_all_generation gaussian(identity) 5 -112.481
## mod_all_null gaussian(identity) 4 -114.217
## mod_all_interaction_line_generation + gaussian(identity) 72 6.924
## AICc delta weight
## mod_all_interaction 208.0 0.00 0.633
## mod_all_fruit 209.6 1.64 0.279
## mod_all_fruit_generation 211.9 3.98 0.087
## mod_all_line 220.1 12.14 0.001
## mod_line_generation 223.5 15.50 0.000
## mod_all_generation 235.6 27.60 0.000
## mod_all_null 236.8 28.87 0.000
## mod_all_interaction_line_generation 439.3 231.37 0.000
## Models ranked by AICc(x)
## Random terms (all models):
## '1 | Generation', '1 | Generation:Fruit_s'summary(mod_all_interaction)## Linear mixed model fit by maximum likelihood ['lmerMod']
## Formula:
## fitness ~ Fruit_s * Generation + (1 | Generation) + (1 | Generation:Fruit_s)
## Data: data_sum_allfruits_AN
## Weights: N
##
## AIC BIC logLik deviance df.resid
## 199.2 250.0 -80.6 161.2 88
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8179 -0.7264 0.0873 0.6984 2.2653
##
## Random effects:
## Groups Name Variance Std.Dev.
## Generation:Fruit_s (Intercept) 0.000 0.000
## Generation (Intercept) 0.000 0.000
## Residual 1.813 1.346
## Number of obs: 107, groups: Generation:Fruit_s, 25; Generation, 4
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -1.2579 1.4340 -0.877
## Fruit_sCherry 0.1344 1.4604 0.092
## Fruit_sCranberry 1.7136 1.4583 1.175
## Fruit_sFig 1.1471 1.4909 0.769
## Fruit_sGrape -3.4559 3.1334 -1.103
## Fruit_sRosehips -2.6107 1.6847 -1.550
## Fruit_sStrawberry 0.5629 1.4584 0.386
## Fruit_sTomato -1.5720 1.7459 -0.900
## Generation -0.5607 0.6930 -0.809
## Fruit_sCherry:Generation 0.7404 0.6988 1.060
## Fruit_sCranberry:Generation 0.2725 0.6979 0.391
## Fruit_sFig:Generation 0.2539 0.7097 0.358
## Fruit_sGrape:Generation 1.6698 1.5329 1.089
## Fruit_sRosehips:Generation 1.3420 0.8036 1.670
## Fruit_sStrawberry:Generation 0.6427 0.6980 0.921
## Fruit_sTomato:Generation 1.1111 0.8101 1.371
## convergence code: 0
## boundary (singular) fit: see ?isSingular#Posthoc
mod_all_interaction <- lme4::lmer(fitness ~ Fruit_s*Generation + (1|Generation) + (1|Generation:Fruit_s),
weights = N, data = data_sum_allfruits_AN)
summary(mod_all_interaction)## Linear mixed model fit by REML ['lmerMod']
## Formula:
## fitness ~ Fruit_s * Generation + (1 | Generation) + (1 | Generation:Fruit_s)
## Data: data_sum_allfruits_AN
## Weights: N
##
## REML criterion at convergence: 184.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.59867 -0.66991 0.08051 0.64407 2.08904
##
## Random effects:
## Groups Name Variance Std.Dev.
## Generation:Fruit_s (Intercept) 0.000 0.00
## Generation (Intercept) 0.000 0.00
## Residual 2.132 1.46
## Number of obs: 107, groups: Generation:Fruit_s, 25; Generation, 4
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) -1.2579 1.5550 -0.809
## Fruit_sCherry 0.1344 1.5836 0.085
## Fruit_sCranberry 1.7136 1.5813 1.084
## Fruit_sFig 1.1471 1.6166 0.710
## Fruit_sGrape -3.4559 3.3977 -1.017
## Fruit_sRosehips -2.6107 1.8268 -1.429
## Fruit_sStrawberry 0.5629 1.5815 0.356
## Fruit_sTomato -1.5720 1.8931 -0.830
## Generation -0.5607 0.7515 -0.746
## Fruit_sCherry:Generation 0.7404 0.7577 0.977
## Fruit_sCranberry:Generation 0.2725 0.7568 0.360
## Fruit_sFig:Generation 0.2539 0.7695 0.330
## Fruit_sGrape:Generation 1.6698 1.6622 1.005
## Fruit_sRosehips:Generation 1.3420 0.8714 1.540
## Fruit_sStrawberry:Generation 0.6427 0.7569 0.849
## Fruit_sTomato:Generation 1.1111 0.8785 1.265
## convergence code: 0
## boundary (singular) fit: see ?isSingularemt1 <- emmeans::emtrends(mod_all_interaction, "Fruit_s", var = "Generation")
emt1 ### estimated slopes of Generation for each level of Fruit_s## Fruit_s Generation.trend SE df lower.CL upper.CL
## Blackcurrant -0.561 0.7937 83.7 -2.1391 1.0177
## Cherry 0.180 0.0985 1460.0 -0.0135 0.3729
## Cranberry -0.288 0.0910 1415.8 -0.4666 -0.1097
## Fig -0.307 0.1706 606.9 -0.6418 0.0282
## Grape 1.109 1.5179 105.0 -1.9006 4.1189
## Rosehips 0.781 0.4558 116.4 -0.1214 1.6840
## Strawberry 0.082 0.0921 1262.4 -0.0987 0.2627
## Tomato 0.550 0.4740 107.6 -0.3892 1.4900
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95pairs(emt1) ### comparison of slopes## contrast estimate SE df t.ratio p.value
## Blackcurrant - Cherry -0.7404 0.800 85.8 -0.926 0.9827
## Blackcurrant - Cranberry -0.2725 0.799 85.5 -0.341 1.0000
## Blackcurrant - Fig -0.2539 0.810 88.8 -0.313 1.0000
## Blackcurrant - Grape -1.6698 1.669 103.4 -1.000 0.9734
## Blackcurrant - Rosehips -1.3420 0.905 90.1 -1.483 0.8142
## Blackcurrant - Strawberry -0.6427 0.799 85.5 -0.804 0.9924
## Blackcurrant - Tomato -1.1111 0.910 86.7 -1.221 0.9235
## Cherry - Cranberry 0.4678 0.133 1205.0 3.505 0.0111
## Cherry - Fig 0.4865 0.196 703.8 2.481 0.2055
## Cherry - Grape -0.9295 1.521 105.8 -0.611 0.9987
## Cherry - Rosehips -0.6016 0.465 124.6 -1.294 0.8996
## Cherry - Strawberry 0.0977 0.134 1138.9 0.728 0.9962
## Cherry - Tomato -0.3707 0.483 114.6 -0.768 0.9944
## Cranberry - Fig 0.0187 0.193 694.9 0.097 1.0000
## Cranberry - Grape -1.3973 1.521 105.7 -0.919 0.9836
## Cranberry - Rosehips -1.0694 0.464 123.5 -2.306 0.2990
## Cranberry - Strawberry -0.3701 0.129 1112.5 -2.872 0.0792
## Cranberry - Tomato -0.8385 0.481 113.7 -1.742 0.6600
## Fig - Grape -1.4160 1.526 107.1 -0.928 0.9827
## Fig - Rosehips -1.0881 0.482 132.2 -2.259 0.3242
## Fig - Strawberry -0.3888 0.193 682.1 -2.013 0.4735
## Fig - Tomato -0.8572 0.498 121.0 -1.721 0.6739
## Grape - Rosehips 0.3278 1.576 107.1 0.208 1.0000
## Grape - Strawberry 1.0271 1.521 105.7 0.675 0.9975
## Grape - Tomato 0.5588 1.578 107.2 0.354 1.0000
## Rosehips - Strawberry 0.6993 0.464 123.5 1.507 0.8023
## Rosehips - Tomato 0.2309 0.639 98.0 0.361 1.0000
## Strawberry - Tomato -0.4684 0.482 113.7 -0.973 0.9774
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 8 estimates4.3 Plot
pd <- position_dodge(0.3) # move them .05 to the left and right
#Extract slope and intercept
dat_8fruits <- expand.grid(Generation=as.numeric(levels(as.factor(data_sum_allfruits_AN$Generation))),
Fruit_s=unique(data_sum_allfruits_AN$Fruit_s))
dat_8fruits$fitness_predicted <- predict(mod_all_interaction, newdata = dat_8fruits,
re.form= NA, type = "response")
PLOT_CHERRY <- ggplot(data = data_sum_allfruits[data_sum_allfruits$Fruit_s == "Cherry",],
aes(x = factor(Generation), group = Line, y = fitness, colour =Fruit_s)) +
geom_errorbar(aes(ymin = fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
width=.1,position = pd, size = 0.4,color = "black") +
geom_line(size = 0.5,position = pd) +
geom_line(data = dat_8fruits[dat_8fruits$Fruit_s == "Cherry",],
aes(x = factor(Generation), y = fitness_predicted, colour = "black", group=NA), size = 0.7) +
geom_point(size = 2, position = pd, shape = 21, fill = "white",stroke = 1.2) +
ylim(-5.65, 1.25) +
ylab("Fitness") +
xlab("Generation") +
xlim("1","2","3","4","5") +
scale_color_manual(values = c("black", "#BC3C6D")) +
ggtitle("Cherry") +
theme_LO_adaptation + theme(plot.title = element_text(color = "#BC3C6D"))
PLOT_CHERRY
PLOT_CRANBERRY <- ggplot(data = data_sum_allfruits[data_sum_allfruits$Fruit_s == "Cranberry",],
aes(x = factor(Generation), group = Line, y = fitness, colour =Fruit_s)) +
geom_errorbar(aes(ymin = fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
width=.1,position = pd, size = 0.4,color = "black") +
geom_line(size = 0.5,position = pd) +
geom_line(data = dat_8fruits[dat_8fruits$Fruit_s == "Cranberry",],
aes(x = factor(Generation), y = fitness_predicted, colour = "black", group=NA), size = 0.7) +
geom_point(size = 2, position = pd, shape = 21, fill = "white",stroke = 1.2) +
ylim(-5.65, 1.25) +
ylab("Fitness") +
xlim("1","2","3","4","5") +
xlab("Generation") +
scale_color_manual(values = c("black","#FDB424")) +
ggtitle("Cranberry") +
theme_LO_adaptation + theme(plot.title = element_text(color = "#FDB424"))
PLOT_CRANBERRY
PLOT_STRAWBERRY <- ggplot(data = data_sum_allfruits[data_sum_allfruits$Fruit_s == "Strawberry",],
aes(x = factor(Generation), group = Line, y = fitness, colour =Fruit_s)) +
geom_errorbar(aes(ymin = fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
width=.1,position = pd, size = 0.4,color = "black") +
geom_line(size = 0.5,position = pd) +
geom_line(data = dat_8fruits[dat_8fruits$Fruit_s == "Strawberry",],
aes(x = factor(Generation), y = fitness_predicted, colour = "black", group=NA), size = 0.7) +
geom_point(size = 2, position = pd, shape = 21, fill = "white",stroke = 1.2) +
ylim(-5.65, 1.25) +
xlim("1","2","3","4","5") +
ylab("Fitness") +
xlab("Generation") +
scale_color_manual(values = c("black","#3FAA96")) +
ggtitle("Strawberry") +
theme_LO_adaptation + theme(plot.title = element_text(color = "#3FAA96"))
PLOT_STRAWBERRY
PLOT_GRAPE <- ggplot(data = data_sum_allfruits[data_sum_allfruits$Fruit_s == "Grape",],
aes(x = factor(Generation), group = Line, y = fitness, colour =Fruit_s)) +
geom_errorbar(aes(ymin = fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
width=.1,position = pd, size = 0.4,color = "black") +
geom_line(size = 0.5,position = pd) +
geom_line(data = dat_8fruits[dat_8fruits$Fruit_s == "Grape"&dat_8fruits$Generation == c(2,3),],
aes(x = factor(Generation), y = fitness_predicted, colour = "black", group=NA), size = 0.7) +
geom_point(size = 2, position = pd, shape = 21, fill = "white",stroke = 1.2) +
ylim(-5.65, 1.25) +
xlim("1","2","3","4","5") +
ylab("Fitness") +
xlab("Generation") +
scale_color_manual(values = c("black","#7B7554")) +
ggtitle("Grape") +
theme_LO_adaptation + theme(plot.title = element_text(color = "#7B7554"))
PLOT_GRAPE
PLOT_ROSEHIPS <- ggplot(data = data_sum_allfruits[data_sum_allfruits$Fruit_s == "Rosehips",],
aes(x = factor(Generation), group = Line, y = fitness, colour =Fruit_s)) +
geom_errorbar(aes(ymin = fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
width=.1,position = pd, size = 0.4,color = "black") +
geom_line(size = 0.5,position = pd) +
geom_line(data = dat_8fruits[dat_8fruits$Fruit_s == "Rosehips"&dat_8fruits$Generation != 5,],
aes(x = factor(Generation), y = fitness_predicted, colour = "black", group=NA), size = 0.7) +
geom_point(size = 2, position = pd, shape = 21, fill = "white",stroke = 1.2) +
ylim(-5.65, 1.25) +
xlim("1","2","3","4","5") +
ylab("Fitness") +
xlab("Generation") +
scale_color_manual(values = c("black","#8ACDEA")) +
ggtitle("Rose hips") +
theme_LO_adaptation + theme(plot.title = element_text(color = "#8ACDEA"))
PLOT_ROSEHIPS
PLOT_TOMATO <- ggplot(data = data_sum_allfruits[data_sum_allfruits$Fruit_s == "Tomato",],
aes(x = factor(Generation), group = Line, y = fitness, colour =Fruit_s)) +
geom_errorbar(aes(ymin = fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
width=.1,position = pd, size = 0.4,color = "black") +
geom_line(size = 0.5,position = pd) +
geom_line(data = dat_8fruits[dat_8fruits$Fruit_s == "Tomato"&dat_8fruits$Generation != 5,],
aes(x = factor(Generation), y = fitness_predicted, colour = "black", group=NA), size = 0.7) +
geom_point(size = 2, position = pd, shape = 21, fill = "white", stroke = 1.2) +
ylim(-5.65, 1.25) +
xlim("1","2","3","4","5") +
ylab("Fitness") +
xlab("Generation") +
scale_color_manual(values = c( "black","#ED6F47")) +
ggtitle("Tomato") +
theme_LO_adaptation + theme(plot.title = element_text(color = "#ED6F47"))
PLOT_TOMATO
PLOT_FIG <- ggplot(data = data_sum_allfruits[data_sum_allfruits$Fruit_s == "Fig",],
aes(x = factor(Generation), group = Line, y = fitness, colour =Fruit_s)) +
geom_errorbar(aes(ymin = fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
width=.1,position = pd, size = 0.4,color = "black") +
geom_line(size = 0.5,position = pd) +
geom_line(data = dat_8fruits[dat_8fruits$Fruit_s == "Fig"&dat_8fruits$Generation != 5,],
aes(x = factor(Generation), y = fitness_predicted, colour = "black", group=NA), size = 0.7) +
geom_point(size = 2, position = pd, shape = 21, fill = "white", stroke = 1.2) +
ylim(-5.65, 1.25) +
xlim("1","2","3","4","5") +
ylab("Fitness") +
xlab("Generation") +
scale_color_manual(values = c("black","#93194F")) +
ggtitle("Fig") +
theme_LO_adaptation + theme(plot.title = element_text(color = "#93194F"))
PLOT_FIG
PLOT_BLACKCURRANT <- ggplot(data = data_sum_allfruits[data_sum_allfruits$Fruit_s == "Blackcurrant",],
aes(x = factor(Generation), group = Line, y = fitness, colour =Fruit_s)) +
geom_errorbar(aes(ymin = fitness-1.96*se_fitness, ymax = fitness + 1.96*se_fitness),
width=.1,position = pd, size = 0.4,color = "black") +
geom_line(size = 0.5,position = pd) +
geom_line(data = dat_8fruits[dat_8fruits$Fruit_s == "Blackcurrant"&dat_8fruits$Generation == c(2,3),],
aes(x = factor(Generation), y = fitness_predicted, colour = "black", group=NA), size = 0.7) +
geom_point(size = 2, position = pd, shape = 21, fill = "white", stroke = 1.2) +
ylim(-5.65, 1.25) +
xlim("1","2","3","4","5") +
ylab("Fitness") +
xlab("Generation") +
scale_color_manual(values = c( "black","#203753")) +
ggtitle("Blackcurrant") +
theme_LO_adaptation + theme(plot.title = element_text(color = "#203753"))
PLOT_BLACKCURRANT
DYNAMIC_EIGHT_JOIN <- cowplot::ggdraw() +
cowplot::draw_plot(PLOT_STRAWBERRY, x = 0, y = 0, width = 0.5, height = 0.28) +
cowplot::draw_plot(PLOT_GRAPE+ theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line.x = element_blank()),
x = 0, y = 0.28, width = 0.5, height = 0.23) +
cowplot::draw_plot(PLOT_CRANBERRY+ theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line.x = element_blank()),
x = 0, y = 0.53, width = 0.5, height = 0.23) +
cowplot::draw_plot(PLOT_BLACKCURRANT+ theme(axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line.x = element_blank()),
x = 0, y = 0.77, width = 0.5, height = 0.23) +
cowplot::draw_plot(PLOT_TOMATO+ theme(axis.title.y = element_blank()),
x = 0.5, y = 0, width = 0.5, height = 0.28) +
cowplot::draw_plot(PLOT_ROSEHIPS+ theme(axis.title.y = element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line.x = element_blank()),
x = 0.5, y = 0.28, width = 0.5, height = 0.23) +
cowplot::draw_plot(PLOT_FIG+ theme(axis.title.y = element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line.x = element_blank()),
x = 0.5, y = 0.53, width = 0.5, height = 0.23) +
cowplot::draw_plot(PLOT_CHERRY + theme(axis.title.y = element_blank(),
axis.title.x = element_blank(),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.line.x = element_blank()),
x = 0.5, y = 0.77, width = 0.5, height = 0.23) +
cowplot::draw_plot_label(c("A", "C", "E", "G", "B", "D", "F", "H"),
x = c(0, 0, 0, 0, 0.5, 0.5, 0.5, 0.5),
y = c(1, 0.75, 0.50, 0.27, 1, 0.75, 0.5, 0.27),
hjust = c(-0.5, -0.5, -0.5,-0.5, -0.5, -0.5, -0.5, -0.5),
vjust = c(1.5, 1.5, 1.5,1.5, 1.5, 1.5,1.5, 1.5),
size = 12)
DYNAMIC_EIGHT_JOIN
cowplot::save_plot(file =here::here("figures", "SUPMAT_Fitness8Fruits.pdf"), DYNAMIC_EIGHT_JOIN, base_height = 17/cm(1), base_width = 15/cm(1), dpi = 1200)5 SUP: Pooling effect
5.1 Data
########### Hypothesis 1
#Hypothesis 1: effect of pool
#Take the mean of all populations for each selection fruit
CheCran_hypo1 <- computelogchange_POOL_hypo1(data_G7, "Cherry", "Cranberry")
########### Hypothesis 2
#Hypothesis 2: effect of pool
#Take the fitness difference of the population with the highest fitness in G7
TEMP_dataG7_CheCran <- formattinglogchange(data_logchange, "7", "Cherry", "Cranberry")
fittest_lines<-computelogchange_POOL_hypo2(data_G7, "Cherry", "Cranberry")
CheCran_hypo2<-TEMP_dataG7_CheCran[TEMP_dataG7_CheCran$Line==fittest_lines,]5.2 Plot
#Initial plot from main text
ymin = -50
ymax = 50
# Limits
ymin_CheCranG29=min(min(TEMP_dataG29_CheCran$logchange_allop-1.96*TEMP_dataG29_CheCran$sd_allop, na.rm= TRUE),
min(TEMP_dataG29_CheCran$logchange_symp-1.96*TEMP_dataG29_CheCran$sd_symp, na.rm= TRUE))
ymax_CheCranG29=max(max(TEMP_dataG29_CheCran$logchange_allop + 1.96*TEMP_dataG29_CheCran$sd_allop, na.rm= TRUE),
max(TEMP_dataG29_CheCran$logchange_symp + 1.96*TEMP_dataG29_CheCran$sd_symp, na.rm= TRUE))
CheCran_G29 <- ggplot(data = TEMP_dataG29_CheCran) +
geom_errorbar(aes(x =logchange_symp, ymin = logchange_allop-(1.96*sd_allop),
ymax = logchange_allop + (1.96*sd_allop),
color = Symp),
width= 0.02, size = 0.3, alpha = 0.8) +
geom_errorbarh(aes(y = logchange_allop, xmin = logchange_symp-1.96*sd_symp, xmax = logchange_symp + 1.96*sd_symp,
color = Symp),
height = 0.02, size = 0.3, alpha = 0.8) +
geom_point(aes(x =logchange_symp, y = logchange_allop, color = Symp,fill = Symp, shape = Allop),
size =3, fill = "white", stroke =1.2) +
xlab("Fitness difference in\nselective environment") +
ylab("Fitness difference in\nalternative environment") +
ggtitle("Cherry vs. Cranberry") +
labs(shape = "Test fruit", color = "Evolution fruit") +
scale_shape_manual(values = c(16, 15)) +
scale_color_manual(values = c("#BC3C6D", "#FDB424")) +
coord_cartesian(ylim = c(ymin_CheCranG29, ymax_CheCranG29),
xlim = c(ymin_CheCranG29, ymax_CheCranG29)) +
theme_LO_sober
CheCran_G29
PLOT_HYPO_1<-CheCran_G29 +
geom_errorbar(data = CheCran_hypo1, aes(x =logchange_symp, ymin = logchange_allop-(1.96*sd_allop),
ymax = logchange_allop + (1.96*sd_allop),
color = Symp),
width= 0.02, size = 0.2, alpha =1) +
geom_errorbarh(data = CheCran_hypo1,
aes(y = logchange_allop, xmin = logchange_symp-1.96*sd_symp, xmax = logchange_symp + 1.96*sd_symp,
color = Symp),
height = 0.02, size = 0.2, alpha =1) +
geom_point(data = CheCran_hypo1,
aes(x =logchange_symp, y = logchange_allop, color = Symp,fill = Symp, shape = Allop),
size = 6, fill = "white", stroke = 2) +
geom_point(data = CheCran_hypo1,
aes(x =logchange_symp, y = logchange_allop, fill = Symp, shape = Allop),
size = 3, fill = "white", color = "white", stroke = 2) +
ggtitle("Hypothesis 1: Mean of populations effect")
PLOT_HYPO_1
PLOT_HYPO_2<-CheCran_G29 +
geom_errorbar(data = CheCran_hypo2, aes(x = logchange_symp,
ymin = logchange_allop-(1.96*sd_allop),
ymax = logchange_allop + (1.96*sd_allop),
color = Symp),
width= 0.02, size = 0.2, alpha =1) +
geom_errorbarh(data = CheCran_hypo2,
aes(y = logchange_allop, xmin = logchange_symp-1.96*sd_symp, xmax = logchange_symp + 1.96*sd_symp,
color = Symp),
height = 0.02, size = 0.2, alpha =1) +
geom_point(data = CheCran_hypo2,
aes(x =logchange_symp, y = logchange_allop, color = Symp,fill = Symp, shape = Allop),
size = 6, fill = "white", stroke = 2) +
geom_point(data = CheCran_hypo2,
aes(x = logchange_symp, y = logchange_allop, fill = Symp, shape = Allop),
size = 3, fill = "white", color = "white", stroke = 2) +
ggtitle("Hypothesis 2: Fittest population effect")
PLOT_HYPO_2
#legend1
legend_tradevide<- ggplot(CheCran_hypo2, aes(x=logchange_symp, y=logchange_allop, group=Fruit_s, colour=Fruit_s)) +
geom_point(size=1.5, position=pd, fill="white", alpha=0.6) +
geom_errorbar(aes(ymin=logchange_allop-(1.96*sd_allop), ymax=logchange_allop+(1.96*sd_allop),),
width=0.4,size=0.4, alpha=0.3) +
labs(colour = "Population \nevolved on:") +
scale_color_manual(values=c("#BC3C6D", "#FDB424","#3FAA96"),
label=c("Cherry","Cranberry", "Strawberry")) +
theme_LO_sober +
theme(legend.title = element_text(colour="black", size=10, face="bold"),
legend.text = element_text(colour="black", size=8))
legend_tradevide<-lemon::g_legend(legend_tradevide)
#legend hypo 1
legend_hypo1<- ggplot(CheCran_hypo2, aes(x=logchange_symp, y=logchange_allop, group=Fruit_s, colour=Fruit_s)) +
geom_point(size=1.5, position=pd, fill="white", alpha=0.6) +
geom_errorbar(aes(ymin=logchange_allop-(1.96*sd_allop), ymax=logchange_allop+(1.96*sd_allop),),
width=0.4,size=0.4, alpha=0.3) +
labs(colour = "Mean of population\nevolved on:") +
geom_point(size=2, shape=21, fill="white", stroke = 2) +
scale_color_manual(values=c("#BC3C6D", "#FDB424","#3FAA96"),
label=c("Cherry","Cranberry", "Strawberry")) +
theme_LO_sober +
theme(legend.title = element_text(colour="black", size=10, face="bold"),
legend.text = element_text(colour="black", size=8))
extract_legend_hypo1<-lemon::g_legend(legend_hypo1)
#legend2
legend_hypo2<- ggplot(CheCran_hypo2, aes(x=logchange_symp, y=logchange_allop, group=Fruit_s, colour=Fruit_s)) +
geom_point(size=1.5, position=pd, fill="white", alpha=0.6) +
geom_errorbar(aes(ymin=logchange_allop-(1.96*sd_allop), ymax=logchange_allop+(1.96*sd_allop),),
width=0.4,size=0.4, alpha=0.3) +
labs(colour = "Population with \nthe highest fitness \non each selection fruit:") +
geom_point(size=2, shape=21, fill="white", stroke = 2) +
scale_color_manual(values=c("#BC3C6D", "#FDB424","#3FAA96"),
label=c("Cherry","Cranberry", "Strawberry")) +
theme_LO_sober +
theme(legend.title = element_text(colour="black", size=10, face="bold"),
legend.text = element_text(colour="black", size=8))
extract_legend_hypo2<-lemon::g_legend(legend_hypo2)
ERRORCROSSES_G7_HYPO1_2 <- cowplot::ggdraw() +
cowplot::draw_plot(PLOT_HYPO_1+theme(legend.position = "none"),
x =0.00, y = 0.5, width = 0.75, height = 0.5) +
cowplot::draw_plot(PLOT_HYPO_2+theme(legend.position = "none"),
x = 0.0, y = 0, width = 0.75, height = 0.5) +
cowplot::draw_plot(legend_tradevide, x = 0.78, y = 0.85, width = 0.1, height = 0.1) +
cowplot::draw_plot(extract_legend_hypo1, x = 0.82, y = 0.7, width = 0.1, height = 0.1) +
cowplot::draw_plot(legend_tradevide, x = 0.76, y = 0.35, width = 0.1, height = 0.1) +
cowplot::draw_plot(extract_legend_hypo2, x = 0.82, y = 0.2, width = 0.1, height = 0.1)
ERRORCROSSES_G7_HYPO1_2
cowplot::save_plot(file =here::here("figures", "SUPMAT_Pooleffect_Hypothesis.pdf"),
ERRORCROSSES_G7_HYPO1_2,
base_height = 18/cm(1), base_width = 15/cm(1), dpi = 610)5.3 Extra plot
PAIR_StrawChe_G29
PAIR_CranStraw_G29
PAIR_CheCran_G29
## Data
# EC_Cran_Cher_G7<-formattingplotpair(data_logchange, "7", "Cherry", "Cranberry")
# EC_Straw_Cran_G7<-formattingplotpair(data_logchange, "7", "Cranberry", "Strawberry")
# EC_Straw_Cher_G7<-formattingplotpair(data_logchange, "7", "Strawberry", "Cherry")
########### Hypothesis 1
#Hypothesis 1: effect of pool
#Take the mean of all populations for each selection fruit
dataset_hypo1 <- formatting_POOL_hypo1(fitness_dataset_intermediate = data_G7)
########### Hypothesis 2
#Hypothesis 2: effect of pool
#Take the fitness difference of the population with the highest fitness in G7
fittest_lines<-formatting_POOL_hypo2(data_G7)
EC_Cran_Cher_hypo2<-EC_Cran_Cher_G7[EC_Cran_Cher_G7$Line == fittest_lines[1]|
EC_Cran_Cher_G7$Line == fittest_lines[2]|
EC_Cran_Cher_G7$Line == fittest_lines[3],]
EC_Straw_Cran_hypo2<-EC_Straw_Cran_G7[EC_Straw_Cran_G7$Line == fittest_lines[1]|
EC_Straw_Cran_G7$Line == fittest_lines[2]|
EC_Straw_Cran_G7$Line == fittest_lines[3],]
EC_Straw_Cher_hypo2<-EC_Straw_Cher_G7[EC_Straw_Cher_G7$Line == fittest_lines[1]|
EC_Straw_Cher_G7$Line == fittest_lines[2]|
EC_Straw_Cher_G7$Line == fittest_lines[3],]
PAIR_CheCran_G29_HYPO_1 <- PAIR_CheCran_G29 +
geom_errorbar(data = dataset_hypo1,
aes(x=logchange_Strawberry,
ymin = logchange_Cranberry-1.96*sd_logchange_Cranberry,
ymax = logchange_Cranberry+1.96*sd_logchange_Cranberry, Line=NA),
width=0.02, size=0.2, alpha=1) +
geom_errorbarh(data = dataset_hypo1,
aes(y=logchange_Cranberry,
xmin = logchange_Strawberry-1.96*sd_logchange_Strawberry,
xmax = logchange_Strawberry+1.96*sd_logchange_Strawberry),
height=0.02, size=0.2, alpha=1) +
geom_point(data = dataset_hypo1,
aes(x = logchange_Strawberry, y = logchange_Cranberry),
shape=21, size=4, fill = "#ffffff", stroke=1.5)
PAIR_CheCran_G29_HYPO_1
PAIR_StrawChe_G29_HYPO_1<-PAIR_StrawChe_G29 +
geom_errorbar(data = dataset_hypo1,
aes(x=logchange_Strawberry,
ymin = logchange_Cranberry-1.96*sd_logchange_Cranberry,
ymax = logchange_Cranberry+1.96*sd_logchange_Cranberry, Line=NA),
width=0.02, size=0.2, alpha=1) +
geom_errorbarh(data = dataset_hypo1,
aes(y=logchange_Cranberry,
xmin = logchange_Strawberry-1.96*sd_logchange_Strawberry,
xmax = logchange_Strawberry+1.96*sd_logchange_Strawberry),
height=0.02, size=0.2, alpha=1) +
geom_point(data = dataset_hypo1,
aes(x = logchange_Strawberry, y = logchange_Cranberry),
shape=21, size=4, fill = "#ffffff", stroke=1.5)
PAIR_StrawChe_G29_HYPO_1
PAIR_CranStraw_G29_HYPO_1<-PAIR_StrawChe_G29 +
geom_errorbar(data = dataset_hypo1,
aes(x=logchange_Strawberry,
ymin = logchange_Cranberry-1.96*sd_logchange_Cranberry,
ymax = logchange_Cranberry+1.96*sd_logchange_Cranberry, Line=NA),
width=0.02, size=0.2, alpha=1) +
geom_errorbarh(data = dataset_hypo1,
aes(y=logchange_Cranberry,
xmin = logchange_Strawberry-1.96*sd_logchange_Strawberry,
xmax = logchange_Strawberry+1.96*sd_logchange_Strawberry),
height=0.02, size=0.2, alpha=1) +
geom_point(data = dataset_hypo1,
aes(x = logchange_Strawberry, y = logchange_Cranberry),
shape=21, size=4, fill = "#ffffff", stroke=1.5)
PAIR_CranStraw_G29_HYPO_1
PAIR_CranStraw_G29_HYPO_2 <- PAIR_CranStraw_G29 +
geom_errorbar(data = EC_Straw_Cran_hypo2,
aes(x=logchange_Strawberry,
ymin = logchange_Cranberry-1.96*sd_logchange_Cranberry,
ymax = logchange_Cranberry+1.96*sd_logchange_Cranberry),
width=0.02, size=0.2, alpha=1) +
geom_errorbarh(data = EC_Straw_Cran_hypo2,
aes(y=logchange_Cranberry,
xmin = logchange_Strawberry-1.96*sd_logchange_Strawberry,
xmax = logchange_Strawberry+1.96*sd_logchange_Strawberry),
height=0.02, size=0.2, alpha=1) +
geom_point(data = EC_Straw_Cran_hypo2,
aes(x = logchange_Strawberry, y = logchange_Cranberry),
shape=21, size=4, fill = "#ffffff", stroke=1.5)
PAIR_CranStraw_G29_HYPO_2
PAIR_StrawChe_G29_HYPO_2 <- PAIR_StrawChe_G29 +
geom_errorbar(data = EC_Straw_Cher_hypo2,
aes(x=logchange_Cherry,
ymin = logchange_Strawberry-1.96*sd_logchange_Strawberry,
ymax = logchange_Strawberry+1.96*sd_logchange_Strawberry),
width=0.02, size=0.2, alpha=1) +
geom_errorbarh(data = EC_Straw_Cher_hypo2,
aes(y=logchange_Strawberry,
xmin = logchange_Cherry-1.96*sd_logchange_Cherry,
xmax = logchange_Cherry+1.96*sd_logchange_Cherry),
height=0.02, size=0.2, alpha=1) +
geom_point(data = EC_Straw_Cher_hypo2,
aes(x = logchange_Cherry, y = logchange_Strawberry),
shape=21, size=4, fill = "#ffffff", stroke=1.5)
PAIR_StrawChe_G29_HYPO_2
PAIR_CheCran_G29_HYPO_2<-PAIR_CheCran_G29 +
geom_errorbar(data = EC_Cran_Cher_hypo2,
aes(x=logchange_Cranberry,
ymin = logchange_Cherry-1.96*sd_logchange_Cherry,
ymax = logchange_Cherry+1.96*sd_logchange_Cherry),
width=0.02, size=0.2, alpha=1) +
geom_errorbarh(data = EC_Cran_Cher_hypo2,
aes(y=logchange_Cherry,
xmin = logchange_Cranberry-1.96*sd_logchange_Cranberry,
xmax = logchange_Cranberry+1.96*sd_logchange_Cranberry),
height=0.02, size=0.2, alpha=1) +
geom_point(data = EC_Cran_Cher_hypo2,
aes(x = logchange_Cranberry, y = logchange_Cherry),
shape=21, size=4, fill = "#ffffff", stroke=1.5)
PAIR_CheCran_G29_HYPO_2
#legend1
legend_tradevide<- ggplot(EC_Cran_Cher_hypo2, aes(x=logchange_Cranberry,
y=logchange_Cherry, group=Fruit_s, colour=Fruit_s)) +
geom_point(size=1.5, position=pd, fill="white", alpha=0.6) +
labs(colour = "Population \nevolved on:") +
scale_color_manual(values=c("#BC3C6D", "#FDB424","#3FAA96"),
label=c("Cherry","Cranberry", "Strawberry")) +
theme_LO_sober +
theme(legend.title = element_text(colour="black", size=10, face="bold"),
legend.text = element_text(colour="black", size=8))
legend_tradevide<-lemon::g_legend(legend_tradevide)
#legend hypo 1
legend_hypo1<- ggplot(EC_Cran_Cher_hypo2, aes(x=logchange_Cranberry,
y=logchange_Cherry, group=Fruit_s, colour=Fruit_s)) +
geom_point(size=1.5, position=pd, fill="white", alpha=0.6) +
labs(colour = "Mean of population\nevolved on:") +
geom_point(size=2, shape=21, fill="white", stroke = 2) +
scale_color_manual(values=c("#BC3C6D", "#FDB424","#3FAA96"),
label=c("Cherry","Cranberry", "Strawberry")) +
theme_LO_sober +
theme(legend.title = element_text(colour="black", size=10, face="bold"),
legend.text = element_text(colour="black", size=8))
extract_legend_hypo1<-lemon::g_legend(legend_hypo1)
#legend2
legend_hypo2<- ggplot(EC_Cran_Cher_hypo2, aes(x=logchange_Cranberry,
y=logchange_Cherry, group=Fruit_s, colour=Fruit_s)) +
geom_point(size=1.5, position=pd, fill="white", alpha=0.6) +
labs(colour = "Population with \nthe highest fitness \non each selection fruit:") +
geom_point(size=2, shape=21, fill="white", stroke = 2) +
scale_color_manual(values=c("#BC3C6D", "#FDB424","#3FAA96"),
label=c("Cherry","Cranberry", "Strawberry")) +
theme_LO_sober +
theme(legend.title = element_text(colour="black", size=10, face="bold"),
legend.text = element_text(colour="black", size=8))
extract_legend_hypo2<-lemon::g_legend(legend_hypo2)
ERRORCROSSES_G7_HYPO1 <- cowplot::ggdraw() +
cowplot::draw_plot(PAIR_CheCran_G29_HYPO_1+theme(legend.position = "none"),
x =0.00, y = 0, width = 0.25, height = 1) +
cowplot::draw_plot(PAIR_CranStraw_G29_HYPO_1+theme(legend.position = "none"),
x = 0.26, y = 0, width = 0.25, height = 1) +
cowplot::draw_plot(PAIR_StrawChe_G29_HYPO_1+theme(legend.position = "none"),
x = 0.52, y = 0, width = 0.25, height = 1) +
cowplot::draw_plot(legend_tradevide, x = 0.8, y = 0.73, width = 0.11, height = 0.1) +
cowplot::draw_plot(extract_legend_hypo1, x = 0.83, y = 0.25, width = 0.1, height = 0.1)
ERRORCROSSES_G7_HYPO1
ERRORCROSSES_G7_HYPO2 <- cowplot::ggdraw() +
cowplot::draw_plot(PAIR_CheCran_G29_HYPO_2+theme(legend.position = "none"),
x =0.00, y = 0, width = 0.25, height = 1) +
cowplot::draw_plot(PAIR_CranStraw_G29_HYPO_2+theme(legend.position = "none"),
x = 0.26, y = 0, width = 0.25, height = 1) +
cowplot::draw_plot(PAIR_StrawChe_G29_HYPO_2+theme(legend.position = "none"),
x = 0.52, y = 0, width = 0.25, height = 1) +
cowplot::draw_plot(legend_tradevide, x = 0.78, y = 0.73, width = 0.1, height = 0.1) +
cowplot::draw_plot(extract_legend_hypo2, x = 0.82, y = 0.25, width = 0.1, height = 0.1)
ERRORCROSSES_G7_HYPO2
#
# cowplot::save_plot(file =here::here("figures", "SUPMAT_Pool_Hypothesis1.pdf"),
# ERRORCROSSES_G7_HYPO1,
# base_height = 7/cm(1), base_width = 28/cm(1), dpi = 610)
#
#
# cowplot::save_plot(file =here::here("figures", "SUPMAT_Pool_Hypothesis2.pdf"),
# ERRORCROSSES_G7_HYPO2,
# base_height = 7/cm(1), base_width = 28/cm(1), dpi = 610)